Next Article in Journal
A Comparative Experimental Design and Performance Analysis of Snort-Based Intrusion Detection System in Practical Computer Networks
Previous Article in Journal
Wearable Food Intake Monitoring Technologies: A Comprehensive Review
Article Menu

Export Article

Open AccessArticle
Computers 2017, 6(1), 5; doi:10.3390/computers6010005

Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm

1
Electrical and Computer Engineering Department, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
2
Electrical and Computer Engineering Faculty, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
3
Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Tehran, 163171419, Iran
*
Author to whom correspondence should be addressed.
Academic Editor: Kartik Gopalan
Received: 21 November 2016 / Revised: 12 January 2017 / Accepted: 24 January 2017 / Published: 28 January 2017
View Full-Text   |   Download PDF [1576 KB, uploaded 8 February 2017]   |  

Abstract

In many non-deterministic search algorithms, particularly those analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. The BA is a population-based metaheuristic search algorithm inspired by bees seeking nectar/pollen. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 well-known benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much more quickly. View Full-Text
Keywords: bees algorithm; Swarm Intelligence; evolutionary optimization; grouped bees algorithm bees algorithm; Swarm Intelligence; evolutionary optimization; grouped bees algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Nasrinpour, H.R.; Bavani, A.M.; Teshnehlab, M. Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm. Computers 2017, 6, 5.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top